THE SEMINAR OF MASTER PROJECT The Implementation of Feedforward-Feedback Fuzzy Logic Algorithm for Level Control System at Process Mini-Plant Measurement

THE SEMINAR OF MASTER THE SEMINAR OF MASTER PROJECTPROJECT

““The Implementation of Feedforward-Feedback The Implementation of Feedforward-Feedback Fuzzy Logic Algorithm for Level Control System at Process Mini-PlantFuzzy Logic Algorithm for Level Control System at Process Mini-Plant

Measurement Laboratory FH-Lausitz “Measurement Laboratory FH-Lausitz “

FachHochschule LausitzUniversity of Applied Sciences

byby

R.Danu Setyo NugrohoR.Danu Setyo Nugroho

(Matrikel.Nr 222743)(Matrikel.Nr 222743)

SupervisorSupervisor

Prof.Dr.Ing. E.SteinProf.Dr.Ing. E.Stein

Co-SupervisorCo-Supervisor

Dipl.Ing (FH) Mario SaderDipl.Ing (FH) Mario Sader

Senftenberg, 7th July 2004


Topic DiscussionDiscussion :

1. Introduction1. Introduction

2. Basic Control Theory2. Basic Control Theory

3. Fuzzy Logic Algorithm Theory3. Fuzzy Logic Algorithm Theory

4. Fuzzy Logic Control Design4. Fuzzy Logic Control Design

5. Validation5. Validation

6. Summary6. Summary


INTRODUCTIONINTRODUCTION

• BackgroundBackground

Process Mini-Plant at Measurement LaboratoryProcess Mini-Plant at Measurement Laboratory

ON/OFF Control ModeON/OFF Control Mode

Set PointSet Point


Set PointSet Point

qin

qout

To keep the set point : qTo keep the set point : qinin = q = qoutout

CONTINUOUS CONTROLCONTINUOUS CONTROL

• The GoalThe Goal




• ProblemsProblems

Lack of Parameter Systems InformationLack of Parameter Systems Information

∑∑ GGcc GGmm

GGtt

ProcessProcessSPSP

+-

e

controllercontroller control valvecontrol valve

level transmitterlevel transmitter

mini plantmini plant

Block Diagram of Close Loop SystemsBlock Diagram of Close Loop Systems

??

??

1)(

)(2

SCRSmC

C

sFa

sXTF

mmm

m



Set PointSet Point

• ProblemsProblems

Load ChangeChange

Normal LoadNormal Load

Load ChangeLoad Change

ErrorError

qqin in > q> qoutoutqqinin should be reduced should be reduced

Set point ChangingSet point Changing

New Set PointNew Set Point

qinin should be increased



• SOLUTIONSOLUTION

Feedforward – FeedbackFeedforward – FeedbackFuzzy Logic ControlFuzzy Logic Control


• process characteristicsprocess characteristics

BASIC CONTROL SYSTEMSBASIC CONTROL SYSTEMS

timetime

Step ResponseStep Response

95%95%

63%63%

ττdd

ττcc

ττrr

dead time dead time ττdd time constant time constant ττcc response time response time ττrr

actual level


• criteria of good controlcriteria of good control


SP2SP2

CV2CV2

SP1SP1

CV1CV1

timetime

timetime

quarter amplitude decayquarter amplitude decay critical dampingcritical damping minimum absolute errorminimum absolute error

tt00

tt00

aa a/a/44

minimum absolute errorminimum absolute error

∫∫|E|dt= minimum|E|dt= minimum


• criteria of good controlcriteria of good control


critical dampingcritical damping

SP2SP2

CV2CV2

SP1SP1

CV1CV1

timetime

timetime

tt00

tt00

over dampingover damping

under dampingunder damping

critical dampingcritical damping


• control systemscontrol systems


feedback control systemsfeedback control systems

feedforward control systemsfeedforward control systems

valvevalve sprayedsprayed

9090oo

180180oo

270270oo

5m5m10m10m15m15m

calibration setcalibration set

controllercontroller processprocesssp

disturbance


FUZZY LOGIC ALGORITHMFUZZY LOGIC ALGORITHM

• historyhistory

The concept of Fuzzy Logic (FL) was conceived by Lotfi Zadeh, a professor at The concept of Fuzzy Logic (FL) was conceived by Lotfi Zadeh, a professor at the University of California at Berkley, and presented not as a control the University of California at Berkley, and presented not as a control methodology, but as a way of processing data by allowing methodology, but as a way of processing data by allowing fuzzy setfuzzy set membershipmembership rather than rather than crisp set membershipcrisp set membership or non-membership. or non-membership.

• advantagesadvantages

free of mathematic modeling systemsfree of mathematic modeling systems( e.g Laplace transform, transfer function systems are not required)( e.g Laplace transform, transfer function systems are not required)

empirically-based on operator’s experience rather than technicalempirically-based on operator’s experience rather than technical understanding of control systemsunderstanding of control systems

( The advance knowledge of control theory is not required)( The advance knowledge of control theory is not required)

flexible and easy in designflexible and easy in design (e.g MIMO,MISO,SISO, rule base determination, simple aritmethic)(e.g MIMO,MISO,SISO, rule base determination, simple aritmethic)

funfun



• Fuzzy Set Vs Crisps SetFuzzy Set Vs Crisps Set

707000CC

warmwarm hothot

FFss : X : X [[0,10,1]]

[1][1]

[0][0]

Crisps Set (Crisps Set (FFS S ))How about T = 69How about T = 69ooC ?C ?

Upss , it is warm !, it isn’t hot at all !Upss , it is warm !, it isn’t hot at all !

Are you happy with that ?Are you happy with that ?

warmwarm

252500CC 757500CC 656500CC 858500CC

hothot

Fuzzy Set (Fuzzy Set (μμff))

μfμf : X : X ||0,10,1||



• The Properties of Fuzzy Set The Properties of Fuzzy Set

11

0.50.5

00

μμff(x)(x)

50503030 707000 100100

coldcold warmwarm hothot

ooCC

universal of discorseuniversal of discorse

scope domainscope domain

LabelLabel

crisps inputcrisps input

deg

re o

f fu

zzy

deg

re o

f fu

zzy

mem

ber

ship

fu

nctio

nm

emb

ersh

ip f

unc

tion



• Operation LogicOperation Logic

BooleanBooleanLogicalLogical

FuzzyFuzzyLogicalLogical

The most common used operation logicThe most common used operation logic



Fuzzy Inference EngineFuzzy Inference Engine

fuzzyfication defuzzyficationrule evaluation

rule base

crispscrispsinputsinputs

crispscrispsoutputsoutputs

controllercontroller



Simple Fuzzy Logic ApplicationSimple Fuzzy Logic ApplicationHome sprinkler systemHome sprinkler system

How long the watering duration should take?How long the watering duration should take?

It depends on the air temperature and soil moistureIt depends on the air temperature and soil moisture

Air temperatureAir temperature

Soil moistureSoil moisture

..FUZZY FUZZY durationduration



Fuzzification for air temperatureFuzzification for air temperature

coolcool warmwarm hothot11

00

μμ

3030 5050 8080 Temp/CTemp/C

fuzzification

CC

WW

HH

T_inT_in6060

6060

3030

μμhh = (temp_in – 50) / gradient = (temp_in – 50) / gradient

μμhh = (60 – 50) / 30 = (60 – 50) / 30 μμhh = 0.33 = 0.33

0.330.33

0.330.33

-30-30

μμww = (temp_in – 80) / gradient = (temp_in – 80) / gradient

μμww = (60 – 80) / -30 = (60 – 80) / -30 μμww = 0.66 = 0.66

0.660.660.660.66

00



Fuzzification for soil moistureFuzzification for soil moisture

0.430.43

0.560.56

drydry moistmoist wetwet11

00

μμ

00 1515 2525 Moist%Moist%

fuzzification

DD

MM

WW

T_inT_in8 %8 %

88

3030

00-30-30

0.560.56

0.430.43



Rule EvaluationRule Evaluation

Which knowledge base should be used ?Which knowledge base should be used ?

operator’s experiences

1.1. If temperature is hot AND moister is wet THEN watering duration is shortIf temperature is hot AND moister is wet THEN watering duration is short2.2. If temperature is hot AND moister is moist THEN watering duration is mediumIf temperature is hot AND moister is moist THEN watering duration is medium3.3. If temperature is hot AND moister is dry THEN watering duration is longIf temperature is hot AND moister is dry THEN watering duration is long4.4. If temperature is warm AND moister is wet THEN watering duration is shortIf temperature is warm AND moister is wet THEN watering duration is short5.5. If temperature is warm AND moister is moist THEN watering duration is mediumIf temperature is warm AND moister is moist THEN watering duration is medium6.6. If temperature is warm AND moister is dry THEN watering duration is longIf temperature is warm AND moister is dry THEN watering duration is long7.7. If temperature is cool AND moister is wet THEN watering duration is shortIf temperature is cool AND moister is wet THEN watering duration is short8.8. If temperature is cool AND moister is moist THEN watering duration is mediumIf temperature is cool AND moister is moist THEN watering duration is medium9.9. If temperature is cool AND moister is dry THEN watering duration is longIf temperature is cool AND moister is dry THEN watering duration is long

If “antecedence 1” AND “antecedence 2 “ THEN “consequent”



Rule EvaluationRule Evaluation

coolcool warmwarm hothot rulerulestrengthstrength

wetwet

moistmoist

drydry

temptemp

moisturemoisture

00

0.560.56

0.430.43

00 0.660.66 0.330.33

Mamdani Min-Max OperationMamdani Min-Max Operation

If temperature is warm (0.66) AND moisture is moist (0.56) THEN watering duration is medium (0.56)

If temperature is hot (0.33) AND moisture is dry (0.43) THEN watering duration is long (0.33)

Y = Min (a,b)

0.560.56

0.330.33

SS SS SS

MM MM MM

LL LL LL

==

==

==

==

==

==

==

==

==

00

00

00

00

0.430.43

00

0.330.33

SS SS SS

MM MM MM

LL LL LL

Y = Max (a,b)

0.560.56

0.430.43

00



Singleton DefuzzificationSingleton Defuzzification

watering duration watering duration (min)(min)

0

1

μ short medium long

S

M

L

time

defuzzification

0

0.56

0.43

20 40 60

0.43

0.56

COGCOG

Center Of Gravity = 0 x 20 + 0.56 x 40 + 0.43 x 60 Center Of Gravity = 0 x 20 + 0.56 x 40 + 0.43 x 60

0 + 0.56 + 0.430 + 0.56 + 0.43(COG)(COG)

= 48.2 minute= 48.2 minute


Strategy of Control DesignStrategy of Control Design

diagram block systemdiagram block system

∑∑ ProcessProcessSPSP

+ -

e FeedbackFeedbackFuzzy ControllerFuzzy Controller

∑∑+

+

FeedforwardFeedforwardFuzzy ControllerFuzzy Controller

Level Membership FunctionLevel Membership Function

μ(f)μ(f)

19,3019,30 29,3529,35 37,9037,90 50,3550,35

very lowvery low lowlow mediummedium highhigh very highvery high

h(cmh(cm))

11

0012,6012,60



fuzzification of feedforward systemsfuzzification of feedforward systems

The degree of membership function |1,0|

very low low medium high very high

18 0,21 0,782 0 0 0

20 0 0,949 0,05 0 0

25 0 0,543 0,45 0 0

28 0 0,137 0,86 0 0

36 0 0 0,22 0,77 0

Level (cm)



rule evaluation of feedforward systemsrule evaluation of feedforward systems

1. IF the level is 1. IF the level is “very high”“very high” THEN the opening of valve is THEN the opening of valve is “very big”“very big” 2. IF the level is 2. IF the level is “high”“high” THEN the opening of valve is THEN the opening of valve is “big”“big”3. IF the level is 3. IF the level is “medium”“medium” THEN the opening of valve is THEN the opening of valve is “medium”“medium”4. IF the level is 4. IF the level is “low”“low” THEN the opening of valve is THEN the opening of valve is “small”“small”5. IF the level is “5. IF the level is “very lowvery low” THEN the opening of valve is ” THEN the opening of valve is “very small”“very small”

1/2

““big”big”

1

0

opening of valveopening of valve

1

0

1/2

opening of valveopening of valve

““big”big”

THEN

levellevel

1

0

1/2

““high”high”

IFIF

fuzzification.vi defuzzification.vi

5 rule



defuzzification of feedforward systemdefuzzification of feedforward system

88 99

very smallvery small smallsmall mediummedium bigbig very bigvery big

6 6 77 voltvolt

1

0 5 5

μ(f)

Opening Valve Membership Opening Valve Membership FunctionFunction

Set Point The degree of membership function [1,0] ControlSignal

very low low medium high very high

18 0 0,938 0,06 0 0 5,78 V

20 0 0,54 0,36 0 0 6,05 V

25 0 0 0,4 0,6 0 6,55 V

28 0 0 0 0,6 0,4 6,86 V

30 0 0 0 0,1 0,9 7,77 V

Level (cm)



fuzzification of feedback systemfuzzification of feedback system

NBNB NSNS ZEZE PSPS PBPB

e

1

0

Error membership functionError membership function

μ(f) LNBLNB PVBPVB XLNBXLNB

-0,5 0 0.5 1,5-1,5 1 -1 -2

The degree of membership function [1,0]

XLNB LNB NB NS ZE PS PB LPB

-2 1 0 0 0 0 0 0 0

-0,9 0 0 0,8 0,2 0 0 0 0

-0,2 0 0 0 0.4 0.6 0 0 0

0.5 0 0 0 0 0 1 0 0

1,2 0 0 0 0 0 0 0,4 0,6

1,5 0 0 0 0 0 0 0 1

error (cm)



rule evaluation of feedback systemrule evaluation of feedback system

1.1.IF error is IF error is “XL Negative Big”“XL Negative Big” THEN corrective valve is THEN corrective valve is “XL Negative Big”“XL Negative Big”2.2.IF error is IF error is “Large Neg. Big”“Large Neg. Big” THEN correction valve is THEN correction valve is “Large Neg. Big ““Large Neg. Big “3.3.IF error is IF error is “Negative Big”“Negative Big” THEN correction valve is THEN correction valve is “Negative Big”“Negative Big”4.4.IF error is IF error is “Negative Small”“Negative Small” THEN correction valve is THEN correction valve is “ Negative Small”“ Negative Small”5.5.IF error is IF error is “Zero Error”“Zero Error” THEN correction valve is THEN correction valve is “Zero”“Zero”6.6.IF error is IF error is “Positive Small”“Positive Small” THEN correction valve is THEN correction valve is “Positive Small”“Positive Small”7.7.IF error is IF error is “Positive Big”“Positive Big” THEN correction valve is THEN correction valve is “Positive Big”“Positive Big”8.8.If error is If error is “Large Pos Big”“Large Pos Big” THEN correction valve is THEN correction valve is “Large Pos Big”“Large Pos Big”

Based on P ControllerBased on P Controller



defuzzification of feedback systemdefuzzification of feedback system

NBNB NSNS ZEZE PSPS PBPB

voltvolt

1

0

Defuzzification ofDefuzzification of Corrective ValveCorrective Valve

μ(f)μ(f) LNBLNB LPBLPB XLNBXLNB

-1,5-1,5 00 0.10.1 33-2,2-2,2 1.01.0 -2-2 -3-3

The degree of membership function [1,0]

XLNB LNB NB NS ZE PS PB LPB

-2 1 0 0 0 0 0 0 0 -3

-0,9 0 0 0,8 0,2 0 0 0 0 -1,34

-0,2 0 0 0 0.4 0.6 0 0 0 -0.28

0.5 0 0 0 0 0 1 0 0 0,5

1,2 0 0 0 0 0 0 0,4 0,6 1,8

1,5 0 0 0 0 0 0 0 1 3

Error (cm) correcting

signal

(v)



VALIDATIONVALIDATION

THE COMPARISON OF PERFOMANCE CONTROLLERTHE COMPARISON OF PERFOMANCE CONTROLLER

STEP RESPONS TESTINGSTEP RESPONS TESTING

FUZZY LOGIC Vs PI CONTROLLERFUZZY LOGIC Vs PI CONTROLLER

SET POINT CHANGINGSET POINT CHANGING

LOAD CHANGELOAD CHANGE

Note : Parameter PI Controller are P = 1,33 and I = 120/s.

Step Response Fuzzy Vs PI Controller

0

5

10

15

20

25

30

1 25 49 73 97 121 145 169 193 217 241 265 289 313 337 361 385 409 433 457 481 505 529

Time(s)

Le

ve

l(c

m)

Fuzzy Controller PI Controller

τs

95% of 25



LOAD CHANGE TESTINGLOAD CHANGE TESTING

Ramp Load Change up to 450% Testing Fuzzy Vs PI Controller

24.6

24.8

25

25.2

25.4

25.6

25.8

26

1 19 37 55 73 91 109 127 145 163 181 199 217 235 253 271 289 307 325 343 361 379

Time(s)

Lev

el(c

m)


Note : - 25 cm level is the normal level without load change- Error open loop in 450% load change is 7 cm

errorerror



SET POINT CHANGE TESTINGSET POINT CHANGE TESTING

Tracking Setpoint 25 to 28 Fuzzy Vs PI Controller

24

24.5

25

25.5

26

26.5

27

27.5

28

28.5

29

1 15 29 43 57 71 85 99 113 127 141 155 169 183 197 211 225 239 253 267 281 295

Time(s)

Lev

el(c

m)


Tracking Setpoint 25 to 22 Fuzzy Vs PI Controller

20.5

21

21.5

22

22.5

23

23.5

24

24.5

25

25.5

1 13 25 37 49 61 73 85 97 109 121 133 145 157 169 181 193 205 217 229 241 253

Time(s)

Lev

el(c

m)



SUMMARYSUMMARY

-The performance of Fuzzy Logic Control here is better then PI Controller The performance of Fuzzy Logic Control here is better then PI Controller in transient response. in transient response.

-The performance of PI Controller here is better then Fuzzy Logic Control The performance of PI Controller here is better then Fuzzy Logic Control in steady state responsein steady state response

- The number of fuzzy membership’s label that is used influence the smoothnessThe number of fuzzy membership’s label that is used influence the smoothness of the controller’s reaction. of the controller’s reaction.

- Fuzzy Logic Control is able to avoid both of overshoot and undershoot conditionFuzzy Logic Control is able to avoid both of overshoot and undershoot condition

- Even plant has two tank, it is catagorized as first order systems. Even plant has two tank, it is catagorized as first order systems. Because the second tank doesn’t act as capacitive element during normal process.Because the second tank doesn’t act as capacitive element during normal process.


SUMMARYSUMMARY

RECOMMENDED FUTURE RESEARCH TOPICSRECOMMENDED FUTURE RESEARCH TOPICS

- Fuzzy Logic Control based on the PI controllerFuzzy Logic Control based on the PI controller

- Adaptive Neuro-Fuzzy Logic ControlAdaptive Neuro-Fuzzy Logic Control

- Self Tuning or Gain Scheduling PI Controller using Fuzzy Logic AlgorithmSelf Tuning or Gain Scheduling PI Controller using Fuzzy Logic Algorithm

THANK YOU FOR YOUR ATTENTIONTHANK YOU FOR YOUR ATTENTION

Documents

THE SEMINAR OF MASTER PROJECT The Implementation of Feedforward-Feedback Fuzzy Logic Algorithm for Level Control System at Process Mini-Plant Measurement